Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1015 2018 2018 Assistant Professor & Head, PG Department of Computer Science, Vidhyaa Giri College of Arts & Science Puduvayal, Karaikudi- 630 108, Sivaganga District, Tamilnadu, India Dr.R.PANDI SELVAM Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques.  2022 2022 41 52 10.54216/FPA.070104 https://www.americaspg.com/articleinfo/3/show/1015